The present invention relates to medical imaging of the brain, and particularly to fully automatic segmentation of brain tumors in multi-parametric 3D MR images.
Detection and segmentation of brain tumor in magnetic resonance images is of great importance in medical diagnosis. Reliable and precise tumor segmentation provides helpful information for surgical planning and therapy accessing. However, manual segmentation of brain tumor images commonly used in clinics is time-consuming, labor-intensive and subjective to considerable variation in intra- and inter-rater performance. Accordingly, a method for fully automatic brain tumor segmentation is desirable.
Several tumor segmentation methods have been proposed recently, such as supervised classification based methods, unsupervised clustering based methods and active contour based methods. Supervised classification based methods with spatial regularization achieve promising performance, however, inconsistency between images to be segmented and training images will degrade the segmentation performance. And clustering or active contour based methods always need proper parameter initialization to achieve reliable segmentation. To partially overcome these limitations, graph based interactive segmentation algorithms become popular in medical image segmentation, such as graph-cut, and random BU walks. For these methods, seed points for both target and background region are needed, and their locations and sizes may influence the segmentation result much. The seed selection in 3D image is time-consuming, and not easy when the boundary of target region is fuzzy. Some automatic seed selection methods have been proposed (Li et al., “Segmentation of Brain Tumors in Multi-parametric MR Images via Robust Statistic Information Propagation”, ACCV 2010, pgs. 606-617, and Wang et al., “Full Automatic Brain Tumor Segmentation Using A Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow”, ICIP 2010, pgs. 2553-2556), in which the images are first pre-segmented by supervised classifier, and then thresholding and morphological operations are applied to get the initial target and background region. However, the tuning of the parameters included in the post-processing is not trivial for different images.